Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665130
Title: Feature extraction in volumetric images
Author: Alathari, Thamer
ISNI:       0000 0004 5346 9400
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2015
Availability of Full Text:
Access through EThOS:
Full text unavailable from EThOS. Please try the link below.
Access through Institution:
Abstract:
The increased interest in volumetric images in recent years requires new feature extraction methods for 3D image interpretation. The aim of this study is to provide algorithms that aid the process of detecting and segmenting geometrical objects from volumetric images. Due to high computational expense, such methods have yet to be established in the volumetric space. Only few have tackled this problem using shape descriptors and key-points of a specific shape; those techniques can detect complex shapes rather than simple geometric shapes due to the well defined key-points. Simplifying the data in the volumetric image using a surface detector and surface curvature estimation preserves the important information about the shapes at the same time reducing the computational expense. Whilst the literature describes only the template of the three-dimensional Sobel operator and not its basis, we present an extended version of the Sobel operator, which considers the gradients of all directions to extract an object’s surface, and with clear basis that allows for development of larger operators. Surface curvature descriptors are usually based on geometrical properties of a segmented object rather than on the change in image intensity. In this work, a new approach is described to estimate the surface curvature of objects using local changes of image intensity. The new methods have shown reliable results on both synthetic and on real volumetric images. The curvature and edge data are then processed in two new techniques for evidence gathering to extract a geometrical shape’s main axis or centre point. The accumulated data are taken directly from voxels’ geometrical locations rather than the surface normals as proposed in literature. The new approaches have been applied to detect a cylinder’s axis and spherical shapes. A new 3D line detection based on origin shifting has also been introduced. Accumulating, at every voxel, the angles resulting from a coordinate transform of a Cartesian to spherical system successfully indicates the existence of a 3D line in the volumetric image. A novel method based on using an analogy to pressure is introduced to allow analysis/ visualisation of objects as though they have been separated, when they were actually touching in the original volumetric images. The approach provides a new domain highlighting the connected areas between multiple touching objects. A mask is formed to detach the interconnected objects and remarkable results are achieved. This is applied successfully to isolate coins within an image of a Roman hoard of coins, and other objects. The approach can fail to isolate objects when the space between them appears to be of similar density to the objects themselves. This motivated development of an operator extended by high-pass filtering and morphological operations. This led to more accurate extraction of coins within the Roman hoard, and to successful isolation of femurs in a database of scanned body images enabling better isolation of hip components in replacement therapy.
Supervisor: Nixon, Mark Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.665130  DOI: Not available
Keywords: QA75 Electronic computers. Computer science
Share: